library(tidyverse)
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library(janitor)
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library(gganimate)
library(ggrepel)

Load gapminder data

gapminder <- read_csv("https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder-FiveYearData.csv") %>%
  clean_names()
## Rows: 1704 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (4): year, pop, lifeExp, gdpPercap
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## â„ą Specify the column types or set `show_col_types = FALSE` to quiet this message.

Filter the gapminder dataset so that continent == “Americas”. Use the map function to create a plot of gdp_percap vs life_exp for each year.

In the chunk below, filter the gapminder dataset so that continent == “Americas” and year == 1995; assign the output to an object called amer97.

Plot gdp_percap vs life_exp and map the size of the points to pop and color to country. Set alpha to 0.7 and do not include a legend. Make the y-axis limits 35 to 85 and the x-axis limits 0 to 45000. If you’re feeling ambitious, try using the geom_text_repel function (more info here) to label each point according to country. Create nice axis labels and make the plot title the year by referring directly to the year variable in the amer97 dataset (I provided the code for this below). The goal here is to first make sure the visualization looks how you want.

amer97 <- gapminder %>%
  filter(continent == "Americas", 
         year == 1997)

ggplot(amer97, aes(x = gdp_percap, y = life_exp, color = country), alpha = 0.7) +
  geom_point(aes(size = pop)) +
  geom_text_repel(aes(label = country)) +
  labs(x = "GDP per capita", y = "Life expectancy", 
       title = amer97$year) +
  scale_color_viridis_d() +
  theme_light() +
  theme(legend.position = "none")
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Now create a function where the input is a dataframe for any year of the gapminder dataset and the output is a plot like the one you just made. Make the y-axis limits 35 to 85 and the x-axis limits 0 to 45000. Use the geom_text_repel function to label each point with country. Test the function out using the amer97 dataframe.

plot_gap <- function(data){
ggplot(data, aes(x = gdp_percap, y = life_exp, color = country), alpha = 0.7) +
  geom_point(aes(size = pop)) +
  geom_text_repel(aes(label = country)) +
  labs(x = "GDP per capita", y = "Life expectancy", 
       title = data$year) +
  lims(y = c(35, 85),
       x = c(0, 45000)) +
  scale_color_viridis_d() +
  theme_light() +
  theme(legend.position = "none")
}

plot_gap(amer97)
## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Filter the gapminder dataset so that continent == “Americas” and then use the group_split function to split the resulting dataset into a list, where each element of the list contains a dataframe for each year. Assign the output to an object called amer_list

amer_list <- gapminder %>%
  filter(continent == "Americas") %>%
  group_split(year)

Use the map function to apply the plot_gap() function to each element of amer_list.

map(amer_list, plot_gap)
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## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
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## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
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## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
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## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
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## Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider
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## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Now try to write the plot_map function on the fly as an “anonymous function” within the map function. Map the function over each element of amer_list. Use the tilde (~) to indicate the start of the function. Use .x to represent the function argument.

map(amer_list, ~ggplot(.x, aes(x = gdp_percap, y = life_exp, size = pop, color = country), alpha = 0.7) +
  geom_point() +
  labs(x = "GDP per capita", y = "Life expectancy", 
       title = .x$year) +
  lims(y = c(35, 85),
       x = c(0, 45000)) +
  scale_color_viridis_d() +
  geom_text_repel(aes(label = country)) +
  theme_light() +
  theme(legend.position = "none"))
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## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
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## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Bonus fancy plot: Use the gganimate package to create an animated version of the plot using the amer dataframe created below, where each frame of the animation represents a different year. You don’t need to write any functions or use the map function. A very similar example is on this web page. See if you can use the example as a guide.

amer <- gapminder %>%
  filter(continent == "Americas") %>%
  mutate(year = as.integer(year))

ggplot(amer, aes(x = gdp_percap, y = life_exp, size = pop, color = country), alpha = 0.7) +
  geom_point() +
  lims(y = c(35, 85),
       x = c(0, 45000)) +
  theme(legend.position = "none") +
  scale_color_viridis_d() +
  geom_text_repel(aes(label = country)) +
  theme_light() +
  labs(x = "GDP per capita", y = "Life expectancy", 
       title = "Year: {frame_time}") +
  transition_time(year) +
  ease_aes("linear")